Determination of the size distribution and percentage of broken kernels of rice using flatbed scanning and image analysis

Image processing techniques to estimate weight and morphological parameters for selected wheat refractions

The geometric and color features of agricultural material along with related physical properties are critical to characterize and express its physical quality. The experiments were conducted to classify the physical characteristics like size, shape, color and texture and then workout the relationship between manual observations and using image processing techniques for weight and volume of the four wheat refractions i.e. sound, damaged, shriveled and broken grains of wheat variety PBW 725. A flatbed scanner was used to acquire the images and digital image processing method was used to process the images and output of image analysis was compared with the actual measurements data using digital vernier caliper. A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R² in the range of 0.798–0.947. The individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021–0.045 and 12.56–46.32 g respectively. Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R² in the range of 0.841–0.920. The sphericity of the refractions varied in the range of 0.52–0.71. Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also. A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R² in the range of 0.845–0.945. Various color and grey level co-variance matrix texture features were extracted from acquired images using the open-source Python programming language and OpenCV library which can exploit different machine and deep learning algorithms to properly classify these refractions.

The Influence of Image Processing and Layer-to-Background Contrast on the Reliability of Flatbed Scanner-Based Characterisation of Additively Manufactured Layer Contours

Flatbed scanners (FBSs) provide non-contact scanning capabilities that could be used for the on-machine verification of layer contours in additive manufacturing (AM) processes. Layer-wise contour deviation assessment could be critical for dimensional and geometrical quality improvement of AM parts, because it would allow for close-loop error compensation strategies. Nevertheless, contour characterisation feasibility faces many challenges, such as image distortion compensation or edge detection quality. The present work evaluates the influence of image processing and layer-to-background contrast characteristics upon contour reconstruction quality, under a metrological perspective. Considered factors include noise filtering, edge detection algorithms, and threshold levels, whereas the distance between the target layer and the background is used to generate different contrast scenarios. Completeness of contour reconstruction is evaluated by means of a coverage factor, whereas its accuracy is determined by comparison with a reference contour digitised in a coordinate measuring machine. Results show that a reliable contour characterisation can be achieved by means of a precise adjustment of image processing parameters under low layer-to-background contrast variability. Conversely, under anisotropic contrast conditions, the quality of contour reconstruction severely drops, and the compromise between coverage and accuracy becomes unbalanced. These findings indicate that FBS-based characterisation of AM layers will demand developing strategies that minimise the influence of anisotropy in layer-to-background contrast.

Effect of metal salts on α-amylase-catalyzed hydrolysis of broken rice under a moderate electric field

This study aimed to evaluate the effects of metal salts on α-amylase-catalyzed hydrolysis of broken rice under a moderate electric field (MEF) by monitoring changes in hydrolysis efficiency, temperature, α-amylase activity, starch-metal ion interaction, and the structural and physicochemical properties of hydrolysates. Results showed that metal salts affected the hydrolysis mainly by altering α-amylase activity rather than by inducing thermal effect or interacting with starch. Reducing sugar content reached 125.0 g/L, while α-amylase activity increased by 18.16% when treated with 0.12 mmol/L Ca²⁺. Holes on hydrolysates treated with Ca²⁺ and Mg²⁺ were larger than those treated with Mn²⁺ and Cu²⁺. No M-O bond was formed after the hydrolysis. The crystallinity was slightly increased with the hydrolysis and the values for Ca²⁺- and Mg²⁺-treated samples were larger. The water and oil absorption capacity of the hydrolysate treated with Ca²⁺ was the highest. This study extended the knowledge of the roles of metal ions on MEF-assisted enzymatic hydrolysis and will contribute to the development of an innovative technology for starch modification.

Layer Contour Verification in Additive Manufacturing by Means of Commercial Flatbed Scanners

Industrial adoption of additive manufacturing (AM) processes demands improvement in the geometrical accuracy of manufactured parts. One key achievement would be to ensure that manufactured layer contours match the correspondent theoretical profiles, which would require integration of on-machine measurement devices capable of digitizing individual layers. Flatbed scanners should be considered as serious candidates, since they can achieve high scanning speeds at low prices. Nevertheless, image deformation phenomena reduce their suitability as two-dimensional verification devices. In this work, the possibilities of using flatbed scanners for AM contour verification are investigated. Image distortion errors are characterized and discussed and special attention is paid to the plication effect caused by contact imaging sensor (CIS) scanners. To compensate this phenomena, a new local distortion adjustment (LDA) method is proposed and its distortion correction capabilities are evaluated upon actual layer contours manufactured on a fused filament fabrication (FFF) machine. This proposed method is also compared to conventional global distortion adjustment (GDA). Results reveal quasi-systematic deformations of the images which could be minimized by means of distortion correction. Nevertheless, the irregular nature of such a distortion and the superposition of different errors penalize the use of GDA, to the point that it should not be used with CIS scanners. Conclusions indicate that LDA-based correction would enable the use of flatbed scanners in AM for on-machine verification tasks.

Maize Silage Kernel Fragment Estimation Using Deep Learning-Based Object Recognition in Non-Separated Kernel/Stover RGB Images

Efficient and robust evaluation of kernel processing from corn silage is an important indicator to a farmer to determine the quality of their harvested crop. Current methods are cumbersome to conduct and take between hours to days. We present the adoption of two deep learning-based methods for kernel processing prediction without the cumbersome step of separating kernels and stover before capturing images. The methods show that kernels can be detected both with bounding boxes and at pixel-level instance segmentation. Networks were trained on up to 1393 images containing just over 6907 manually annotated kernel instances. Both methods showed promising results despite the challenging setting, with an average precision at an intersection-over-union of 0.5 of 34.0% and 36.1% on the test set consisting of images from three different harvest seasons for the bounding-box and instance segmentation networks respectively. Additionally, analysis of the correlation between the Kernel Processing Score (KPS) of annotations against the KPS of model predictions showed a strong correlation, with the best performing at r(15) = 0.88, p = 0.00003. The adoption of deep learning-based object recognition approaches for kernel processing measurement has the potential to lower the quality assessment process to minutes, greatly aiding a farmer in the strenuous harvesting season.

Improving Head Rice Yield and Milling Quality: State-of-the-Art and Future Prospects

Increasing paddy yield in rice does not directly translate to enhancing food security because significant decrease in grain yield can happen during postharvest processing of the rice paddy. In parallel with enhancing paddy yield, improving the milling quality of rice is essential in ensuring food security by mitigating the impact of significant losses during the postharvest processing of rice grains. From an industrial standpoint, maximizing the milling recovery of whole grain polished rice is crucial in fetching higher revenues to rice farmers. Significant advances in rice postharvest processing technology have been achieved which are geared toward reducing the incidence of fissures and chalkiness to increase head rice yield (HRY) in rice. The genetic bases of kernel development and grain dimension are also characterized. In addition to these advancements, an integrated phenotyping suite to simultaneously characterize phenotypes related to milling quality will help in screening for breeding lines with high HRY. Toward this goal, modern imaging tools and computer algorithms are currently being developed for high-throughput characterization of rice milling quality. With the availability of more sophisticated, affordable, automated, and nondestructive phenotyping methods of milling quality, it is envisioned that significant improvement in HRY will be made possible to ensure rice food security in the future.

Head rice rate measurement based on concave point matching

Head rice rate is an important factor affecting rice quality. In this study, an inflection point detection-based technology was applied to measure the head rice rate by combining a vibrator and a conveyor belt for bulk grain image acquisition. The edge center mode proportion method (ECMP) was applied for concave points matching in which concave matching and separation was performed with collaborative constraint conditions followed by rice length calculation with a minimum enclosing rectangle (MER) to identify the head rice. Finally, the head rice rate was calculated using the sum area of head rice to the overall coverage of rice. Results showed that bulk grain image acquisition can be realized with test equipment, and the accuracy rate of separation of both indica rice and japonica rice exceeded 95%. An increase in the number of rice did not significantly affect ECMP and MER. High accuracy can be ensured with MER to calculate head rice rate by narrowing down its relative error between real values less than 3%. The test results show that the method is reliable as a reference for head rice rate calculation studies.

Digital image analysis of diverse Mexican rice cultivars

BACKGROUND

Digital image analysis has an important role in geographical provenance of grains, as it can provide parameters of size, shape and color, which are important quality parameters for the design of engineering processes such as drying and milling of grains. In this study, digital image analysis was used to classify nine rice cultivars based on different morphometric parameters using the three sides of the grain (lateral, ventral and axial), Feret diameter, and 10 different form factors and color parameters (CIE L*, a* and b*).

RESULTS

Result of principal component analyisis was an equation with seven variables (area, perimeter, length, width, thickness, sphericity and color), which was useful for distinguishing between nine different cultivars. The morphometric and color parameters for the Mor A-98 and Mor A-92 varieties showed they had 88% similarity. The variability was expressed with a confidence of 95%.

CONCLUSION

Multivariate analysis indicated that the lateral side is the most sensitive for the classification of Mexican rice grains because of its color and morphometric characteristics. These results showed the application of image analysis for the future classifications of grains.